电源学报

基于变分模态分解和核极限学习机集成模型的电动汽车锂电池健康状态预测

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巫春玲 ¹ , 吕晶晶 ¹ , 相里康 ¹ , 孟锦豪 ² , 黄鑫蓉 ¹ , 张震 ¹

电源学报 | 电池与储能 2025,23(6): 288-299

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电源学报 |电池与储能 2025 , 23 (6) : 288 -299

基于变分模态分解和核极限学习机集成模型的电动汽车锂电池健康状态预测

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巫春玲¹, 吕晶晶¹, 相里康¹, 孟锦豪², 黄鑫蓉¹, 张震¹

作者信息

^1.长安大学能源与电气工程学院，西安　710061

^2.西安交通大学电气工程学院，西安　710049

通讯作者:

相里康（1988—　），男，通信作者，博士，讲师。研究方向：新能源车电子制动控制、部件故障诊断。E-mail：kxiangli@chd.edu.cn。

作者简介:

巫春玲（1978—　），女，博士，副教授。研究方向：新能源电动汽车及储能系统电池管理研究。E-mail：wuchl@chd.edu.cn。

吕晶晶（1998—　），女，硕士研究生。研究方向：电池健康状态研究。E-mail：ljj1538364@163.com。

孟锦豪（1988—　），男，博士，副教授。研究方向：储能系统设计与能量管理。E-mail：jinhao@xjtu.edu.cn。

黄鑫蓉（1993—　），女，博士，讲师。研究方向：电池管理系统，电动汽车储能系统。E-mail：xinronghuang2021@163.com。

张震（1986—　），男，博士，讲师。研究方向：电力电子建模与控制。E-mail：zhenzhang@chd.edu.cn。

SOH Prediction of Electric Vehicle Lithium Battery Based on Integrated Model of Variation Modal Decomposition and Kernel-based Extreme Learning Machine

Chunling WU¹, Jingjing LÜ¹, Kang XIANGLI¹, Jinhao MENG², Xinrong HUANG¹, Zhen ZHANG¹

Affiliations

^1.School of Energy and Electrical Engineering, Chang'an University, Xi'an 710061, China

^2.School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China

出版时间: 2025-10-30 doi: 10.13234/j.issn.2095-2805.2025.6.288

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摘要

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在传统电动汽车锂电池预测中，往往将健康状态SOH（state-of-health）预测视作一个整体，进而给出单一SOH预测结果。但在汽车实际运行中，直接进行SOH的单一预测误差大，预测效果不好。为了提高电动汽车电池的SOH预测精度，提出了1种基于变分模态分解和麻雀搜索算法优化的核极限学习机集成模型的新预测方法VMD-SSA-KELM。该方法通过变分模态分解电池SOH序列，降低SOH回升的影响；同时利用Person相关法减少噪声的影响，提高预测的准确性；引入核极限学习机KELM，在保留极限学习机优点的基础上，提高了预测的精度。基于4辆电动汽车的运行数据对提出的模型进行验证，结果表明与VMD-DBO-KELM、VMD-POA-KELM、VMD-KELM、VMD-ELM模型相比，所提模型的预测趋势与原数据趋势一致，其他模型的结果波动较大，新模型预测的均方根误差在0.20%内，预测精度更高，预测效率更快，所用时间更短，故可以证明所提方法具有更高的准确性和鲁棒性。

关键词

锂电池 / 变分模态分解 / 核极限学习机 / 麻雀搜索算法

Abstract

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In the traditional prediction of lithium batteries for electric vehicles, the state-of-health (SOH) prediction is usually regarded as a whole, and the result of single SOH prediction is obtained accordingly. However, in the actual operation of a car, the single prediction of SOH has a large error, and its prediction effect is not satisfying. To improve the accuracy of battery SOH prediction for electric vehicles, a novel prediction method based on variational modal decomposition (VMD) and sparrow search algorithm (SSA) optimization of kernel-based extreme learning machine (KELM) integrated prediction model, i.e., VMD-SSA-KELM, is proposed. First, the battery SOH sequence is decomposed by VMD to reduce the influence of SOH fluctuations. Meanwhile, the Person correlation method is used to reduce the influence of noise and improve the accuracy of prediction. The KELM is introduced, which improves the accuracy of prediction while retaining the advantages of extreme learning machine. The proposed model is validated based on the operation data of four electric vehicles, and experimental results show that compared with the VMD-DBO-KELM, VMD-POA-KELM, VMD-KELM and VMD-ELM models, the proposed model has a prediction trend which is the same as that of the original data, while the results of other models fluctuate a lot. The root mean square error of results predicted by the novel model is less than 0.2%, the prediction accuracy becomes higher, the prediction efficiency is faster and the time used is shorter, indicating that the proposed method has higher accuracy and better robustness.

Key words

Lithium battery / variational modal decomposition (VMD) / kernel-based extreme learning machine (KELM) / sparrow search algorithm (SSA)

引用本文

巫春玲, 吕晶晶, 相里康, 孟锦豪, 黄鑫蓉, 张震. 基于变分模态分解和核极限学习机集成模型的电动汽车锂电池健康状态预测. 电源学报, 2025 , 23 (6) : 288 -299 . DOI: 10.13234/j.issn.2095-2805.2025.6.288

Chunling WU, Jingjing LÜ, Kang XIANGLI, Jinhao MENG, Xinrong HUANG, Zhen ZHANG. SOH Prediction of Electric Vehicle Lithium Battery Based on Integrated Model of Variation Modal Decomposition and Kernel-based Extreme Learning Machine[J]. Journal of Power Supply, 2025 , 23 (6) : 288 -299 . DOI: 10.13234/j.issn.2095-2805.2025.6.288

基金

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国家重点研发计划资助项目(2021YFB2601300)
陕西省重点研发计划资助项目(2022GY-193)
中国博士后科学基金资助项目(2020M683414)
陕西省自然科学基础研究计划资助项目(2021JQ-251)
陕西省科技重大专项课题资助项目(2020zdzx06-01-01)

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2025年第23卷第6期

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文章信息

doi: 10.13234/j.issn.2095-2805.2025.6.288

接收时间：2023-04-18
首发时间：2026-10-26
出版时间：2025-10-30

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出版历史

收稿日期：2023-04-18
修回日期：2023-08-02
录用日期：2023-08-16

基金

National Key R&D Program(2021YFB2601300)

国家重点研发计划资助项目(2021YFB2601300)

Shaanxi Province Key R&D Program(2022GY-193)

陕西省重点研发计划资助项目(2022GY-193)

China Postdoctoral Science Foundation(2020M683414)

中国博士后科学基金资助项目(2020M683414)

Shaanxi Province Natural Science Basic Research Program(2021JQ-251)

陕西省自然科学基础研究计划资助项目(2021JQ-251)

Shaanxi Province Science and Technology Major Special Project(2020zdzx06-01-01)

陕西省科技重大专项课题资助项目(2020zdzx06-01-01)

作者信息

^1.长安大学能源与电气工程学院，西安　710061

^2.西安交通大学电气工程学院，西安　710049

通讯作者:

相里康（1988—　），男，通信作者，博士，讲师。研究方向：新能源车电子制动控制、部件故障诊断。E-mail：kxiangli@chd.edu.cn。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2025.6.288

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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